我正在基于此breast cancer dataset构建ML预测仪表板应用程序。

从下拉菜单中，我希望能够选择我的模型之一，运行拟合，并返回更新的混淆矩阵（热图）。

我计划将脚本扩展到表格，roc-curves，learning-curves等（即multi output callback）-但首先我希望这部分工作，然后再实现其他元素。

我尝试了不同的事情。

例如，在当前代码（如下）之前，我尝试直接从下拉菜单中调用模型，然后在回调内部进行所有cm计算，从而导致 AttributeError：'str'对象具有没有属性“适合” ：

@app.callback(Output('conf_matrix','figure'),[Input('dropdown-5','value')])
def update_cm_matix(model):
    class_names=[0,1]
    fitModel = model.fit(X_train,y_train)
    y_pred = fitModel.predict(X_test)
    cm = confusion_matrix(y_test,y_pred)
    return {'data': [go.Heatmap(x=class_names,y=class_names,z=cm,showscale=True,colorscale='blues')],'layout': dict(width=350,height=280,margin={'t': 10},xaxis=dict(title='Predicted class',tickvals=[0,1]),yaxis=dict(title='True class',1],autorange='reversed'))}

（在下面的脚本中替换app.callback和函数）。

我正在苦苦挣扎的当前版本是：

# -*- coding: utf-8 -*-
import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_bootstrap_components as dbc
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV,train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.feature_selection import RFE
import plotly.graph_objs as go
from dash.dependencies import Input,Output

app = dash.Dash(__name__,external_stylesheets=[dbc.themes.BOOTSTRAP])
server = app.server

app.config.suppress_callback_exceptions = True

df = pd.read_csv("breast_cancer.csv")
y = np.array(df.diagnosis.tolist())
data = df.drop('diagnosis',1)
X = np.array(data.values)

scaler = StandardScaler()
X = scaler.fit_transform(X)

random_state = 42
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=random_state)

# First model: logistic model + optimize hyperparameters
log = LogisticRegression(random_state=random_state)
param_grid = {'penalty': ['l2','l1'],'C': [0.001,0.01,0.1,1,10,100,1000]}
CV_log = GridSearchCV(estimator=log,param_grid=param_grid,scoring='accuracy',verbose=1,n_jobs=-1)
CV_log.fit(X_train,y_train)
log_best_params = CV_log.best_params_
log_clf = LogisticRegression(C=log_best_params['C'],penalty=log_best_params['penalty'],random_state=random_state)

# Second model: logistic model with recursive features elimination (just for illustration purposes,other models will be included)
rfe_selector = RFE(log_clf)

# app layout
app.layout = html.Div([
    html.Div([
        dcc.Dropdown(
            id='dropdown-5',options=[{'label': 'Logistic','value': 'log_clf'},{'label': 'RFE','value': 'rfe_selector'}],value='log_clf',style={'width': '150px','height': '35px','fontSize': '10pt'}
        )],style={}),html.Div([
        dcc.Graph(id='conf_matrix')
    ])
])

# function to run selected model
def ClassTrainEval(model):
    fitModel = model.fit(X_train,y_pred)
    return fitModel,y_pred,y_score,cm

models = [log_clf,rfe_selector]
class_names = [0,1]

# dash callback
@app.callback(Output('conf_matrix','value')])
def update_cm_matix(model):
    for model in models:
        ClassTrainEval(model)
    return {'data': [go.Heatmap(x=class_names,autorange='reversed'))}

if __name__ == '__main__':
    app.run_server(debug=True)

哪里出现 NameError：未定义名称'cm' 错误。

我不确定如何使它正常工作-所以我希望有人可以指出正确的方向。

谢谢！